frmCashDeposit

串口通讯程序，包括打开、设置串口参数,读写串口等 -Serial communication procedures, including open, set the serial port parameters, such as reading and writing serial

该资料为双向boost DCDC变换器的mathcad计算文档，计算过程十分详细和全面。(This data is a Mathcad calculation document of bidirectional boost DCDC converter. The calculation process is very detailed and comprehensive.)

说明：  新手练习，输入月份查看兔子的指数增长，适合练习(Novice practice, enter the month to see the exponential growth of the rabbit)

VGA_LCD 控制器IP,我也是从网上下载的，还没有来得及看，-VGA_LCD controller IP, I also downloaded from the Internet, has not yet had time to look,

说明：  SVM训练的基本程序，学习SVM用的，C++语言，visio平台，学习用(SVM training basic procedures, learning SVM with C + + language, Visio platform, learning with)

Genetic Algorithms, JGAP Example

计算器程序具有加减乘除开方等功能非科学计算器(Calculator program having arithmetic functions such as prescribing non-scientific calculator)

说明：  本文档主要用matlab语言设计了FIR和IIR低通滤波器、高通滤波器和带通滤波器。(In this paper, FIR and IIR low-pass filter, high pass filter and band-pass filter are designed by MATLAB.)

说明：  参考代码，提供给新手学习，代码可以修改完善，不涉及版权！(Reference code, for beginners to learn, code can be modified and improved, not involving copyright!)

说明：  在葡萄酒制造业中，对于葡萄酒的分类具有很大意义，因为这涉及到不同种类的葡萄酒的存放以及出售价格，采用神经网络做为分类器可以有效预测相关葡萄酒的种类，从UCI数据库中得到wine数据记录的是在意大利某一地区同一区域上三种不同品种的葡萄酒的化学成分分析，数据里含有178个样本分别属于三个类别(类别标签已给)，每个样本含有13个特征分量(化学成分)，将这178个样本70%做为训练样本，另30%做为测试样本，用训练样本对神经网络分类器进行训练，用得到的模型对测试样本的进行分类标签预测。(In the wine manufacturing industry, classification of wine is of great significance, because it involves the storage and selling price of different kinds of wine. Using neural network as classifier can effectively predict the types of related wine. The wine data recorded from UCI database is the chemical composition of three different kinds of wine in the same region of Italy According to the analysis, 178 samples in the data belong to three categories (the category label has been given). Each sample contains 13 characteristic components (chemical components). 70% of the 178 samples are used as training samples, and 30% are used as test samples. The training samples are used to train the neural network classifier, and the model is used to predict the classification label of the test samples.)